
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a         =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 							a		(				snake_case__:   Dict		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return (abs(source - target   ) / target) < 0.0_1




@pytest.mark.integration
def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   _TestCommandArgs(dataset=snake_case__		,  all_configs=snake_case__		,  save_infos=snake_case__   )
				lowercase_	    =   TestCommand(*snake_case__   )
				test_command.run()
				lowercase_	    =   os.path.join(snake_case__		,  '''README.md'''   )
				assert os.path.exists(snake_case__   )
				lowercase_	    =   DatasetInfosDict.from_directory(snake_case__   )
				lowercase_	    =   DatasetInfosDict(
				    {
				        '''default''': DatasetInfo(
				            features=Features(
				                {
				                    '''tokens''': Sequence(Value('''string'''   )   ),
				                    '''ner_tags''': Sequence(
				                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']   )   ),
				                    '''langs''': Sequence(Value('''string'''   )   ),
				                    '''spans''': Sequence(Value('''string'''   )   ),
				                }   )		,  splits=[
				                {
				                    '''name''': '''train''',
				                    '''num_bytes''': 2_351_563,
				                    '''num_examples''': 10_000,
				                },
				                {
				                    '''name''': '''validation''',
				                    '''num_bytes''': 238_418,
				                    '''num_examples''': 1_000,
				                },
				            ]		,  download_size=3_940_680		,  dataset_size=2_589_981		,  )
				    }   )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								lowercase_    ,      lowercase_	    =   getattr(dataset_infos['''default''']		,  snake_case__   ), getattr(expected_dataset_infos['''default''']		,  snake_case__   )
								if key == "num_bytes":
												assert is_apercent_close(snake_case__		,  snake_case__   )
								elif key == "splits":
												assert list(snake_case__   ) == list(snake_case__   )
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes		,  expected[split].num_bytes   )
								else:
												result == expected





"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


